Feature based estimation of myocardial motion from tagged MR images

In the past few years we witnessed an increase in mortality due to cancer relative to mortality due to cardiovascular diseases. In 2008, the Netherlands Statistics Agency reports that 33.900 people died of cancer against 33.100 deaths due to cardiovascular diseases, making cancer the number one cause of death in the Netherlands [33]. Even if the rate of people affected by heart diseases is continually rising, they "simply don’t die of it", according to the research director Prof. Mat Daemen of research institute CARIM of the University of Maastricht [50]. The reason for this is the early diagnosis, and the treatment of people with identified risk factors for diseases like ischemic heart disease, hypertrophic cardiomyopathy, thoracic aortic disease, pericardial (sac around the heart) disease, cardiac tumors, pulmonary artery disease, valvular disease, and congenital heart disease before and after surgical repair. Cardiac imaging plays a crucial role in the early diagnosis, since it allows the accurate investigation of a large amount of imaging data in a small amount of time. Moreover, cardiac imaging reduces costs of inpatient care, as has been shown in recent studies [77]. With this in mind, in this work we have provided several tools with the aim to help the investigation of the cardiac motion. In chapters 2 and 3 we have explored a novel variational optic flow methodology based on multi-scale feature points to extract cardiac motion from tagged MR images. Compared to constant brightness methods, this new approach exhibits several advantages. Although the intensity of critical points is also influenced by fading, critical points do retain their characteristic even in the presence of intensity changes, such as in MR imaging. In an experiment in section 5.4 we have applied this optic flow approach directly on tagged MR images. A visual inspection confirmed that the extracted motion fields realistically depicted the cardiac wall motion. The method exploits also the advantages from the multiscale framework. Because sparse velocity formulas 2.9, 3.7, 6.21, and 7.5 provide a number of equations equal to the number of unknowns, the method does not suffer from the aperture problem in retrieving velocities associated to the critical points. In chapters 2 and 3 we have moreover introduced a smoothness component of the optic flow equation described by means of covariant derivatives. This is a novelty in the optic flow literature. Many variational optic flow methods present a smoothness component that penalizes for changes from global assumptions such as isotropic or anisotropic smoothness. In the smoothness term proposed deviations from a predefined motion model are penalized. Moreover, the proposed optic flow equation has been decomposed in rotation-free and divergence-free components. This decomposition allows independent tuning of the two components during the vector field reconstruction. The experiments and the Table of errors provided in 3.8 showed that the combination of the smoothness term, influenced by a predefined motion model, and the Helmholtz decomposition in the optic flow equation reduces the average angular error substantially (20%-25%) with respect to a similar technique that employs only standard derivatives in the smoothness term. In section 5.3 we extracted the motion field of a phantom of which we know the ground truth of and compared the performance of this optic flow method with the performance of other optic flow methods well known in the literature, such as the Horn and Schunck [76] approach, the Lucas and Kanade [111] technique and the tuple image multi-scale optic flow constraint equation of Van Assen et al. [163]. Tests showed that the proposed optic flow methodology provides the smallest average angular error (AAE = 3.84 degrees) and L2 norm = 0.1. In this work we employed the Helmholtz decomposition also to study the cardiac behavior, since the vector field decomposition allows to investigate cardiac contraction and cardiac rotation independently. In chapter 4 we carried out an analysis of cardiac motion of ten volunteers and one patient where we estimated the kinetic energy for the different components. This decomposition is useful since it allows to visualize and quantify the contributions of each single vector field component to the heart beat. Local measurements of the kinetic energy have also been used to detect areas of the cardiac walls with little movement. Experiments on a patient and a comparison between a late enhancement cardiac image and an illustration of the cardiac kinetic energy on a bull’s eye plot illustrated that a correspondence between an infarcted area and an area with very small kinetic energy exists. With the aim to extend in the future the proposed optic flow equation to a 3D approach, in chapter 6 we investigated the 3D winding number approach as a tool to locate critical points in volume images. We simplified the mathematics involved with respect to a previous work [150] and we provided several examples and applications such as cardiac motion estimation from 3-dimensional tagged images, follicle and neuronal cell counting. Finally in chapter 7 we continued our investigation on volume tagged MR images, by retrieving the cardiac motion field using a 3-dimensional and simple version of the proposed optic flow equation based on standard derivatives. We showed that the retrieved motion fields display the contracting and rotating behavior of the cardiac muscle. We moreover extracted the through-plane component, which provides a realistic illustration of the vector field and is missed by 2-dimensional approaches

Cardiac motion and deformation estimation in tagged magnetic resonance imaging

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Electrónica Médica)Cardiovascular diseases are the main cause of death in Europe, with an estimate of 4.3 million deaths each year. The assessment of the regional wall deformation is a relevant clinical indicator, and can be used to detect several cardiac lesions. Nowadays, this study can be performed using several image modalities. In the current thesis, we focus on tagged Magnetic Resonance imaging (t-MRI) technique. Such technique allows acquiring images with tags on the myocardium, which deform with the muscle. The present thesis intends to assess the left ventricle (LV) deformation using radial and circumferential strain. To compute such strain values, both endo- and epicardial contours of the LV are required. As such, a new framework to automatically assess the LV function is proposed. This framework presents: (i) an automatic segmentation technique, based on a tag suppression strategy followed by an active contour segmentation method, and (ii) a tracking approach to extract myocardial deformation, based on a non-rigid registration method. The automatic segmentation uses the B-spline Explicit Active Surface framework, which was previously applied in ultra-sound and cine-MRI images. In both cases, a real-time and accurate contour was achieved. Regarding the registration step, starting from a state-of-art approach, termed sequential 2D, we suggest a new method (termed sequential 2D+t), where the temporal information is included on the model. The tracking methods were first tested on synthetic data to study the registration parameters influence. Furthermore, the proposed and original methods were applied on porcine data with myocardial ischemia. Both methods were able to detect dysfunctional regions. A comparison between the strain curve in the sequential 2D and sequential 2D+t strategies was also shown. As conclusion, a smoothing effect in the strain curve was detected in the sequential 2D+t strategy. The validation of the segmentation approach uses a human dataset. A comparison between the manual contour and the proposed segmentation method results was performed. The results, suggest that proposed method has an acceptable performance, removing the tedious task related with manual segmentation and the intra-observer variability. Finally, a comparison between the proposed framework and the currently available commercial software was performed. The commercial software results were obtained from core-lab analysis. An acceptable result (r = 0.601) was achieved when comparing the strain peak values. Importantly, the proposed framework appears to present a more acceptable result.As doenças cardiovasculares são a principal causa de morte na Europa, com aproximadamente 4.7 milhões de mortes por ano. A avaliação da deformação do miocárdio a um nível local é um importante indicador clínico e pode ser usado para a deteção de lesões cardíacas. Este estudo é normalmente realizado usando várias modalidades de imagem médica. Nesta tese, a Resonância Magnética (RM) marcada foi a técnica selecionada. Estas imagens têm marcadores no músculo cardíaco, os quais se deformam com o miocárdio e podem ser usados para o estudo da deformação cardíaca. Nesta tese, pretende-se estudar a deformação radial e circunferencial do ventrículo esquerdo (VE). Assim, um contorno do endo- e epicárdio no VE é essencial. Desta forma, uma ferramenta para o estudo da deformação do VE foi desenvolvida. Esta possui: (i) um método de segmentação automático, usando uma estratégia de supressão dos marcadores, seguido de uma segmentação c um contorno ativo, e (ii) um método de tracking para determinação da deformação cardíaca, baseado em registo não rígido. A segmentação automática utiliza a ferramenta B-spline Explicit Active Surface, que foi previamente aplicada em imagens de ultrassons e cine-RM. Em ambos os casos, uma segmentação em tempo real e com elevada exatidão foi alcançada. Vários esquemas de registo foram apresentados. Neste ponto, começando com uma técnica do estado da arte (designada de sequencial 2D), uma nova metodologia foi proposta (sequencial 2D+t), onde a informação temporal é incorporada no modelo. De forma a analisar a influência dos parâmetros do registo, estes foram estudados num dataset sintético. De seguida, os diferentes esquemas de registo foram testados num dataset suíno com isquemia. Ambos os métodos foram capazes de detetar as regiões disfuncionais. De igual forma, utilizando as curvas de deformação obtidas para cada um dos métodos propostos, foi possível observar uma suavização na direção temporal para o método sequencial 2D+t. Relativamente à segmentação, esta foi validada com um dataset humano. Um contorno manual foi comparado com o obtido pelo método proposto. Os resultados sugerem que a nova estratégia é aceitável, sendo mais rápida do que a realização de um contorno manual e eliminando a variabilidade entre observadores. Por fim, realizou-se uma comparação entre a ferramenta proposta e um software comercial (com análise de core-lab). A comparação entre os valores de pico da deformação exibe uma correlação plausível (r=0.601). Contudo, é importante notar, que a nova ferramenta tende a apresentar um resultado mais aceitável

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Analysis of myocardial contractility with magnetic resonance

Heart failure has considerable morbidity and poor prognosis. An understanding of the underlying mechanics governing myocardial contraction is a prerequisite for interpreting and predicting changes induced by heart disease. Gross changes in contractile behaviour of the myocardium are readily detected with existing techniques. For more subtle changes during early stages of cardiac dysfunction, however, it requires a sensitive method for measuring, as well as a precise criterion for quantifying, normal and impaired myocardial function. Cardiovascular Magnetic Resonance (CMR) imaging is emerging as an important clinical tool because of its safety, versatility, and the high quality images it produces that allow accurate and reproducible quantification of cardiac structure and function. Traditional CMR approaches for measuring contractility rely on tagging of the myocardium with fiducial markers and require a lengthy and often subjective dependant post-processing procedure. The aim of this research is to develop a new technique, which uses velocity as a marker for the visualisation and assessment of myocardial contractility. Two parallel approaches have been investigated for the assessment of myocardial velocity. The first of these is haimonic phase (HARP) imaging. HARP imaging allows direct derivation of myocardial velocity and strain without the need of further user interaction. We investigated the effect of respiration on the accuracy of the derived contractility, and assessed the clinical applicability and potential pitfalls of the technique by analysing results from a group of patients with hypertrophic cardiomyopathy. The second technique we have investigated is the direct measurement of myocardial velocity with phase contrast myocardial velocity mapping. The imaging sequence used employs effective blood saturation for reducing flow induced phase errors within the myocardium. View sharing was used to improve the temporal resolution, which permitted acquisition of 3D velocity information throughout the cardiac cycle in a single breath-hold, enabling a comprehensive assessment of strain rate of the left ventricle. One key factor that affects the derivation of myocardial contractility based on myocardial velocity is the practical inconsistency of the velocity data. A novel iterative optimisation scheme by incorporating the incompressibility constraint was developed for the restoration of myocardial velocity data. The method allowed accurate assessment of both in-plane and through-plan strain rates, as demonstrated with both synthetic and in vivo data acquired from normal subjects and ischaemic patients. To further enhance the clinical potential of the technique and facilitate the visual assessment of contractile abnormality with myocardial velocity mapping, a complementary analysis framework, named Virtual Tagging, has been developed. The method used velocity data in all directions combined with a finite element mesh incorporating geometrical and physical constraints. The Virtual Tagging framewoik allowed velocity measurements to be used for calculating strain distribution within the 3D volume. It also permitted easy visualisation of the displacement of the tissue, akin to traditional CMR tagging. Detailed validation of the technique is provided, which involves both numerical simulation and in vitro phantom experiments. The main contribution of this thesis is in the improvement of the effectiveness and quality of quantitative myocardial contractility analysis from both sequence design and medical image computing perspectives. It is aimed at providing a sensitive means of detecting subtle as well as gross changes in contractile behaviour of the myocardium. The study is expected to provide a clinically viable platform for functional correlation with other functional measures such as myocardial perfusion and diffusion, and to serve as an aid for further understanding of the links between intrinsicOpen acces

Bridging spatiotemporal scales in biomechanical models for living tissues : from the contracting Esophagus to cardiac growth

Appropriate functioning of our body is determined by the mechanical behavior of our organs. An improved understanding of the biomechanical functioning of the soft tissues making up these organs is therefore crucial for the choice for, and development of, efficient clinical treatment strategies focused on patient-specific pathophysiology. This doctoral dissertation describes the passive and active biomechanical behavior of gastrointestinal and cardiovascular tissue, both in the short and long term, through computer models that bridge the cell, tissue and organ scale. Using histological characterization, mechanical testing and medical imaging techniques, virtual esophagus and heart models are developed that simulate the patient-specific biomechanical organ behavior as accurately as possible. In addition to the diagnostic value of these models, the developed modeling technology also allows us to predict the acute and chronic effect of various treatment techniques, through e.g. drugs, surgery and/or medical equipment. Consequently, this dissertation offers insights that will have an unmistakable impact on the personalized medicine of the future.Het correct functioneren van ons lichaam wordt bepaald door het mechanisch gedrag van onze organen. Een verbeterd inzicht in het biomechanisch functioneren van deze zachte weefsels is daarom van cruciale waarde voor de keuze voor, en ontwikkeling van, efficiënte klinische behandelingsstrategieën gefocust op de patiënt-specifieke pathofysiologie. Deze doctoraatsthesis brengt het passieve en actieve biomechanisch gedrag van gastro-intestinaal en cardiovasculair weefsel, zowel op korte als lange termijn, in kaart via computermodellen die een brug vormen tussen cel-, weefsel- en orgaanniveau. Aan de hand van histologische karakterisering, mechanische testen en medische beeldvormingstechnieken worden virtuele slokdarm- en hartmodellen ontwikkeld die het patiënt-specifieke orgaangedrag zo accuraat mogelijk simuleren. Naast de diagnostische waarde van deze modellen, laat de ontwikkelde modelleringstechnologie ook toe om het effect van verschillende behandelingstechnieken, via medicatie, chirurgie en/of medische apparatuur bijvoorbeeld, acuut en chronisch te voorspellen. Bijgevolg biedt deze doctoraatsthesis inzichten die een onmiskenbare impact zullen hebben op de gepersonaliseerde geneeskunde van de toekomst

Robust and time-effcient determination of perfusion parameters using time-encoded Arterial Spin Labeling MRI

In clinical routine, arterial spin labeling (ASL) faces many challenges, such as time pressure, patient- and disease-specific artifacts, e.g., in steno-occlusive and Moya-Moya disease. In addition, individually tailored parametrization of the MR pulse-sequence is frequently required. Time-encoded ASL-techniques like Hadamard time-encoded pseudocontinuous ASL (H-pCASL) offers a time and signal efficient way to measure accurately both perfusion and arterial transit-times. However, it relies on the decoding of a series of volumes. If even a single volume is corrupted this might, via the decoding process, lead to artifacts in the entire dataset and in the worst case result in the loss of the data. In this thesis a general introduction to time encoded ASL is given and three methods are introduced to increase the robustness of time-encoded ASL against image artifacts and to detect corrupted images. The first method is Walsh-ordered time-encoded H-pCASL (WH-pCASL). It proposes the Walsh-ordering of Hadamard encoding-matrices. In contrast to conventional H-pCASL, this makes perfusion-weighted images accessible during a running experiment and even from incomplete sets of encoded images. An optional additional averaging strategy is based on a mirrored matrix and results in more perfusion-weighted images without any penalty in time. The feasibility of the method is shown using five volunteer datasets. As a second method non-decoded time-encoded ASL is introduced. This novel model-based approach to quantification avoids the decoding step altogether. It models the non-decoded time encoded signal. Therefore it uses the convolution of the tissue response function with a model of the true encoded arterial input function, which is determined by the employed encoding matrix. The model was implemented in a Bayesian model-based ASL analysis framework to fit maps for hemodynamic parameters. The feasibility of the method is demonstrated in a study with five volunteers. The last method is an algorithm for the automated detection of outliers and corrupted images, which is based on variational Bayesian inference (VB). Using the variance of the posterior normal distributions, the algorithm measures the quality of a fit directly and without the need for a separate reference dataset. Its performance and feasibility is demonstrated using volunteer data and a clinical dataset

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention